Attributes of captured objects in a capture system

ABSTRACT

Regular expressions used for searching for patterns in captured objects can be grouped into attributes. Such attributes can be associated with captured objects using tags stored in a database. In one embodiment, the present invention includes capturing an object being transmitted over a network, and determining that a regular expression appears in the object, the regular expression belonging to a group of one or more regular expressions associated with an attribute. If a regular expression associated with the attribute is found in the object, then an attribute field of a tag containing metadata related to the captured object is set to indicate the presence of the attribute in the captured object. The presence of the attribute in the captured object can now be determined from the tag, which can be stored in a database.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation (and claims the benefit of priorityunder 35 U.S.C. §120) of U.S. patent application Ser. No. 12/751,876,filed on Mar. 31, 2010 now U.S. Pat. No. 8,176,049, entitled “ATTRIBUTESOF CAPTURED OBJECTS IN A CAPTURE SYSTEM,” which application is adivisional of U.S. patent application Ser. No. 11/254,436, filed Oct.19, 2005, now issued as U.S. Pat. No. 7,730,011, entitled “ATTRIBUTES OFCAPTURED OBJECTS IN A CAPTURE SYSTEM”. The disclosures of the priorapplications are considered part of (and are incorporated herein byreference in their entirety) the disclosure of this application.

FIELD OF THE INVENTION

The present invention relates to computer networks, and in particular,to a capture device.

BACKGROUND

Computer networks and systems have become indispensable tools for modernbusiness. Modern enterprises use such networks for communications andfor storage. The information and data stored on the network of abusiness enterprise is often a highly valuable asset. Modern enterprisesuse numerous tools to keep outsiders, intruders, and unauthorizedpersonnel from accessing valuable information stored on the network.These tools include firewalls, intrusion detection systems, and packetsniffer devices. However, once an intruder has gained access tosensitive content, there is no network device that can prevent theelectronic transmission of the content from the network to outside thenetwork. Similarly, there is no network device that can analyze the dataleaving the network to monitor for policy violations, and make itpossible to track down information leaks. What is needed is acomprehensive system to capture, store, and analyze all datacommunicated using the enterprises network.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements and in which:

FIG. 1 is a block diagram illustrating a computer network connected tothe Internet;

FIG. 2 is a block diagram illustrating one configuration of a capturesystem according to one embodiment of the present invention;

FIG. 3 is a block diagram illustrating the capture system according toone embodiment of the present invention;

FIG. 4 is a block diagram illustrating an object assembly moduleaccording to one embodiment of the present invention;

FIG. 5 is a block diagram illustrating an object store module accordingto one embodiment of the present invention;

FIG. 6 is a block diagram illustrating an example hardware architecturefor a capture system according to one embodiment of the presentinvention;

FIG. 7 is a block diagram illustrating an object classification moduleaccording to one embodiment of the present invention;

FIG. 8 is a block diagram illustrating bit vector generation accordingto one embodiment of the present invention;

FIG. 9 is a block diagram illustrating an attribute module according toone embodiment of the present invention;

FIG. 10 is a flow diagram illustrating attribute tagging according toone embodiment of the present invention; and

FIG. 11 is a flow diagram illustrating query processing according to oneembodiment of the present invention.

DETAILED DESCRIPTION

Although the present system will be discussed with reference to variousillustrated examples, these examples should not be read to limit thebroader spirit and scope of the present invention. Some portions of thedetailed description that follows are presented in terms of algorithmsand symbolic representations of operations on data within a computermemory. These algorithmic descriptions and representations are the meansused by those skilled in the computer science arts to most effectivelyconvey the substance of their work to others skilled in the art. Analgorithm is here, and generally, conceived to be a self-consistentsequence of steps leading to a desired result. The steps are thoserequiring physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared and otherwise manipulated.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers or the like. It should be borne in mind,however, that all of these and similar terms are to be associated withthe appropriate physical quantities and are merely convenient labelsapplied to these quantities. Unless specifically stated otherwise, itwill be appreciated that throughout the description of the presentinvention, use of terms such as “processing”, “computing”,“calculating”, “determining”, “displaying” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system's registersand memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

As indicated above, one embodiment of the present invention isinstantiated in computer software, that is, computer readableinstructions, which, when executed by one or more computerprocessors/systems, instruct the processors/systems to perform thedesignated actions. Such computer software may be resident in one ormore computer readable media, such as hard drives, CD-ROMs, DVD-ROMs,read-only memory, read-write memory and so on. Such software may bedistributed on one or more of these media, or may be made available fordownload across one or more computer networks (e.g., the Internet).Regardless of the format, the computer programming, rendering andprocessing techniques discussed herein are simply examples of the typesof programming, rendering and processing techniques that may be used toimplement aspects of the present invention. These examples should in noway limit the present invention, which is best understood with referenceto the claims that follow this description.

Networks

FIG. 1 illustrates a simple prior art configuration of a local areanetwork (LAN) 10 connected to the Internet 12. Connected to the LAN 10are various components, such as servers 14, clients 16, and switch 18.There are numerous other known networking components and computingdevices that can be connected to the LAN 10. The LAN 10 can beimplemented using various wireline or wireless technologies, such asEthernet and 802.11b. The LAN 10 may be much more complex than thesimplified diagram in FIG. 1, and may be connected to other LANs aswell.

In FIG. 1, the LAN 10 is connected to the Internet 12 via a router 20.This router 20 can be used to implement a firewall, which are widelyused to give users of the LAN 10 secure access to the Internet 12 aswell as to separate a company's public Web server (can be one of theservers 14) from its internal network, i.e., LAN 10. In one embodiment,any data leaving the LAN 10 towards the Internet 12 must pass throughthe router 20. However, there the router 20 merely forwards packets tothe Internet 12. The router 20 cannot capture, analyze, and searchablystore the content contained in the forwarded packets.

One embodiment of the present invention is now illustrated withreference to FIG. 2. FIG. 2 shows the same simplified configuration ofconnecting the LAN 10 to the Internet 12 via the router 20. However, inFIG. 2, the router 20 is also connected to a capture system 22. In oneembodiment, the router 20 splits the outgoing data stream, and forwardsone copy to the Internet 12 and the other copy to the capture system 22.

There are various other possible configurations. For example, the router20 can also forward a copy of all incoming data to the capture system 22as well. Furthermore, the capture system 22 can be configuredsequentially in front of, or behind the router 20, however this makesthe capture system 22 a critical component in connecting to the Internet12. In systems where a router 20 is not used at all, the capture systemcan be interposed directly between the LAN 10 and the Internet 12. Inone embodiment, the capture system 22 has a user interface accessiblefrom a LAN-attached device, such as a client 16.

In one embodiment, the capture system 22 intercepts all data leaving thenetwork. In other embodiments, the capture system can also intercept alldata being communicated inside the network 10. In one embodiment, thecapture system 22 reconstructs the documents leaving the network 10, andstores them in a searchable fashion. The capture system 22 can then beused to search and sort through all documents that have left the network10. There are many reasons such documents may be of interest, includingnetwork security reasons, intellectual property concerns, corporategovernance regulations, and other corporate policy concerns.

Capture System

One embodiment of the present invention is now described with referenceto FIG. 3. FIG. 3 shows one embodiment of the capture system 22 in moredetail. The capture system 22 includes a network interface module 24 toreceive the data from the network 10 or the router 20. In oneembodiment, the network interface module 24 is implemented using one ormore network interface cards (NIC), e.g., Ethernet cards. In oneembodiment, the router 20 delivers all data leaving the network to thenetwork interface module 24.

The captured raw data is then passed to a packet capture module 26. Inone embodiment, the packet capture module 26 extracts data packets fromthe data stream received from the network interface module 24. In oneembodiment, the packet capture module 26 reconstructs Ethernet packetsfrom multiple sources to multiple destinations for the raw data stream.

In one embodiment, the packets are then provided the object assemblymodule 28. The object assembly module 28 reconstructs the objects beingtransmitted by the packets. For example, when a document is transmitted,e.g. as an email attachment, it is broken down into packets according tovarious data transfer protocols such as Transmission ControlProtocol/Internet Protocol (TCP/IP) and Ethernet. The object assemblymodule 28 can reconstruct the document from the captured packets.

One embodiment of the object assembly module 28 is now described in moredetail with reference to FIG. 4. When packets first enter the objectassembly module, they are first provided to a reassembler 36. In oneembodiment, the reassembler 36 groups—assembles—the packets into uniqueflows. For example, a flow can be defined as packets with identicalSource IP and Destination IP addresses as well as identical TCP Sourceand Destination Ports. That is, the reassembler 36 can organize a packetstream by sender and recipient.

In one embodiment, the reassembler 36 begins a new flow upon theobservation of a starting packet defined by the data transfer protocol.For a TCP/IP embodiment, the starting packet is generally referred to asthe “SYN” packet. The flow can terminate upon observation of a finishingpacket, e.g., a “Reset” or “FIN” packet in TCP/IP. If no finishingpacket is observed by the reassembler 36 within some time constraint, itcan terminate the flow via a timeout mechanism. In an embodiment usingthe TPC protocol, a TCP flow contains an ordered sequence of packetsthat can be assembled into a contiguous data stream by the reassembler36. Thus, in one embodiment, a flow is an ordered data stream of asingle communication between a source and a destination.

The flow assembled by the reassembler 36 can then is provided to aprotocol demultiplexer (demux) 38. In one embodiment, the protocol demux38 sorts assembled flows using the TCP Ports. This can includeperforming a speculative classification of the flow contents based onthe association of well-known port numbers with specified protocols. Forexample, Web Hyper Text Transfer Protocol (HTTP) packets—i.e., Webtraffic—are typically associated with port 80, File Transfer Protocol(FTP) packets with port 20, Kerberos authentication packets with port88, and so on. Thus in one embodiment, the protocol demux 38 separatesall the different protocols in one flow.

In one embodiment, a protocol classifier 40 also sorts the flows inaddition to the protocol demux 38. In one embodiment, the protocolclassifier 40—operating either in parallel or in sequence with theprotocol demux 38—applies signature filters to the flows to attempt toidentify the protocol based solely on the transported data. Furthermore,the protocol demux 38 can make a classification decision based on portnumber, which is subsequently overridden by protocol classifier 40. Forexample, if an individual or program attempted to masquerade an illicitcommunication (such as file sharing) using an apparently benign portsuch as port 80 (commonly used for HTTP Web browsing), the protocolclassifier 40 would use protocol signatures, i.e., the characteristicdata sequences of defined protocols, to verify the speculativeclassification performed by protocol demux 38.

In one embodiment, the object assembly module 28 outputs each floworganized by protocol, which represent the underlying objects. Referringagain to FIG. 3, these objects can then be handed over to the objectclassification module 30 (sometimes also referred to as the “contentclassifier”) for classification based on content. A classified flow maystill contain multiple content objects depending on the protocol used.For example, protocols such as HTTP (Internet Web Surfing) may containover 100 objects of any number of content types in a single flow. Todeconstruct the flow, each object contained in the flow is individuallyextracted, and decoded, if necessary, by the object classificationmodule 30.

The object classification module 30 uses the inherent properties andsignatures of various documents to determine the content type of eachobject. For example, a Word document has a signature that is distinctfrom a PowerPoint document, or an Email document. The objectclassification module 30 can extract out each individual object and sortthem out by such content types. Such classification renders the presentinvention immune from cases where a malicious user has altered a fileextension or other property in an attempt to avoid detection of illicitactivity.

In one embodiment, the object classification module 30 determineswhether each object should be stored or discarded. In one embodiment,this determination is based on a various capture rules. For example, acapture rule can indicate that Web Traffic should be discarded. Anothercapture rule can indicate that all PowerPoint documents should bestored, except for ones originating from the CEO's IP address. Suchcapture rules can be implemented as regular expressions, or by othersimilar means. Several embodiments of the object classification module30 are described in more detail further below.

In one embodiment, the capture rules are authored by users of thecapture system 22. The capture system 22 is made accessible to anynetwork-connected machine through the network interface module 24 anduser interface 34. In one embodiment, the user interface 34 is agraphical user interface providing the user with friendly access to thevarious features of the capture system 22. For example, the userinterface 34 can provide a capture rule authoring tool that allows usersto write and implement any capture rule desired, which are then appliedby the object classification module 30 when determining whether eachobject should be stored. The user interface 34 can also providepre-configured capture rules that the user can select from along with anexplanation of the operation of such standard included capture rules. Inone embodiment, the default capture rule implemented by the objectclassification module 30 captures all objects leaving the network 10.

If the capture of an object is mandated by the capture rules, the objectclassification module 30 can also determine where in the object storemodule 32 the captured object should be stored. With reference to FIG.5, in one embodiment, the objects are stored in a content store 44memory block. Within the content store 44 are files 46 divided up bycontent type. Thus, for example, if the object classification moduledetermines that an object is a Word document that should be stored, itcan store it in the file 46 reserved for Word documents. In oneembodiment, the object store module 32 is integrally included in thecapture system 22. In other embodiments, the object store module can beexternal—entirely or in part—using, for example, some network storagetechnique such as network attached storage (NAS) and storage areanetwork (SAN).

Tag Data Structure

In one embodiment, the content store is a canonical storage location,simply a place to deposit the captured objects. The indexing of theobjects stored in the content store 44 is accomplished using a tagdatabase 42. In one embodiment, the tag database 42 is a database datastructure in which each record is a “tag” that indexes an object in thecontent store 44 and contains relevant information about the storedobject. An example of a tag record in the tag database 42 that indexesan object stored in the content store 44 is set forth in Table 1:

TABLE 1 Field Name Definition MAC Address Ethernet controller MACaddress unique to each capture system Source IP Source Ethernet IPAddress of object Destination IP Destination Ethernet IP Address ofobject Source Port Source TCP/IP Port number of object Destination PortDestination TCP/IP Port number of the object Protocol IP Protocol thatcarried the object Instance Canonical count identifying object within aprotocol capable of carrying multiple data within a single TCP/IPconnection Content Content type of the object Encoding Encoding used bythe protocol carrying object Size Size of object Timestamp Time that theobject was captured Owner User requesting the capture of object (ruleauthor) Configuration Capture rule directing the capture of objectSignature Hash signature of object Tag Signature Hash signature of allpreceding tag fields

There are various other possible tag fields, and some embodiments canomit numerous tag fields listed in Table 1. In other embodiments, thetag database 42 need not be implemented as a database, and a tag neednot be a record. Any data structure capable of indexing an object bystoring relational data over the object can be used as a tag datastructure. Furthermore, the word “tag” is merely descriptive, othernames such as “index” or “relational data store,” would be equallydescriptive, as would any other designation performing similarfunctionality.

The mapping of tags to objects can, in one embodiment, be obtained byusing unique combinations of tag fields to construct an object's name.For example, one such possible combination is an ordered list of theSource IP, Destination IP, Source Port, Destination Port, Instance andTimestamp. Many other such combinations including both shorter andlonger names are possible. In another embodiment, the tag can contain apointer to the storage location where the indexed object is stored.

The tag fields shown in Table 1 can be expressed more generally, toemphasize the underlying information indicated by the tag fields invarious embodiments. Some of these possible generic tag fields are setforth in Table 2:

TABLE 2 Field Name Definition Device Identity Identifier of capturedevice Source Address Origination Address of object DestinationDestination Address of object Address Source Port Origination Port ofobject Destination Port Destination Port of the object Protocol Protocolthat carried the object Instance Canonical count identifying objectwithin a protocol capable of carrying multiple data within a singleconnection Content Content type of the object Encoding Encoding used bythe protocol carrying object Size Size of object Timestamp Time that theobject was captured Owner User requesting the capture of object (ruleauthor) Configuration Capture rule directing the capture of objectSignature Signature of object Tag Signature Signature of all precedingtag fields

For many of the above tag fields in Tables 1 and 2, the definitionadequately describes the relational data contained by each field. Forthe content field, the types of content that the object can be labeledas are numerous. Some example choices for content types (as determined,in one embodiment, by the object classification module 30) are JPEG,GIF, BMP, TIFF, PNG (for objects containing images in these variousformats); Skintone (for objects containing images exposing human skin);PDF, MSWord, Excel, PowerPoint, MSOffice (for objects in these popularapplication formats); HTML, WebMail, SMTP, FTP (for objects captured inthese transmission formats); Telnet, Rlogin, Chat (for communicationconducted using these methods); GZIP, ZIP, TAR (for archives orcollections of other objects); Basic_Source, C++_Source, C_Source,Java_Source, FORTRAN_Source, Verilog_Source, VHDL_Source,Assembly_Source, Pascal_Source, Cobol_Source, Ada_Source, Lisp_Source,Perl_Source, XQuery_Source, Hypertext Markup Language, Cascaded StyleSheets, JavaScript, DXF, Spice, Gerber, Mathematica, Matlab, AllegroPCB,ViewLogic, TangoPCAD, BSDL, C_Shell, K_Shell, Bash_Shell, Bourne_Shell,FTP, Telnet, MSExchange, POP3, RFC822, CVS, CMS, SQL, RTSP, MIME, PDF,PS (for source, markup, query, descriptive, and design code authored inthese high-level programming languages); C Shell, K Shell, Bash Shell(for shell program scripts); Plaintext (for otherwise unclassifiedtextual objects); Crypto (for objects that have been encrypted or thatcontain cryptographic elements); Englishtext, Frenchtext, Germantext,Spanishtext, Japanesetext, Chinesetext, Koreantext, Russiantext (anyhuman language text); Binary Unknown, ASCII Unknown, and Unknown (ascatchall categories).

The signature contained in the Signature and Tag Signature fields can beany digest or hash over the object, or some portion thereof. In oneembodiment, a well-known hash, such as MD5 or SHA1 can be used. In oneembodiment, the signature is a digital cryptographic signature. In oneembodiment, a digital cryptographic signature is a hash signature thatis signed with the private key of the capture system 22. Only thecapture system 22 knows its own private key, thus, the integrity of thestored object can be verified by comparing a hash of the stored objectto the signature decrypted with the public key of the capture system 22,the private and public keys being a public key cryptosystem key pair.Thus, if a stored object is modified from when it was originallycaptured, the modification will cause the comparison to fail.

Similarly, the signature over the tag stored in the Tag Signature fieldcan also be a digital cryptographic signature. In such an embodiment,the integrity of the tag can also be verified. In one embodiment,verification of the object using the signature, and the tag using thetag signature is performed whenever an object is presented, e.g.,displayed to a user. In one embodiment, if the object or the tag isfound to have been compromised, an alarm is generated to alert the userthat the object displayed may not be identical to the object originallycaptured.

Attributes

When a user searches over the objects captured by the capture system 22,it is desirable to make the search as fast as possible. One way to speedup searches is to perform searches over the tag database instead of thecontent store, since the content store will generally be stored on diskand is far more costly both in terms of time and processing power tosearch then a database.

A user query for a pattern is generally in the form of a regularexpression. A regular expression is a string that describes or matches aset of strings, according to certain syntax rules. There are variouswell-known syntax rules such as the POSIX standard regular expressionsand the PERL scripting language regular expressions. Regular expressionsare used by many text editors and utilities to search and manipulatebodies of text based on certain patterns. Regular expressions arewell-known in the art. For example, according to one syntax (Unix), theregular expression 4\d{15} means the digit “4” followed by any fifteendigits in a row. This user query would return all objects containingsuch a pattern.

Certain useful search categories cannot be defined well by a singleregular expression. As an example, a user may want to query all emailscontaining a credit card number. Various credit card companies useddifferent numbering patterns and conventions. A card number for eachcompany can be represented by a regular expression. However, the conceptof credit card number can be represented by a union of all such regularexpressions.

For such categories, the concept of attribute is herein defined. Anattribute, in one embodiment, represents a group of one or more regularexpressions (or other such patterns). The term “attribute” is merelydescriptive, such concept could just as easily be termed “category,”“regular expression list,” or any other descriptive term.

One embodiment of the present invention is now described with referenceto FIG. 7. In the embodiment described with reference to FIG. 7, theattribute tagging functionality is implemented in the objectclassification module 30 described above. However, the attribute taggingprocess and modules may be implemented in other parts of the capturesystem 22 or as a separate module.

FIG. 7 illustrates a detailed diagram of one embodiment of the objectclassification module 30. Objects arriving from the object assemblymodule 28 are forwarded to the content store, and used to generate thetag to be associated with the object. For example, one module called thecontent classifier 62 can determine the content type of the object. Thecontent type is then forwarded to the tag generator 68 where it isinserted into the content field described above. Various other suchprocessing, such as protocol and size determination, is represented byother processing block 66.

In one embodiment, the attribute module 64 generates an attribute indexthat can be inserted into an index field of the tag by the tag generator68. One embodiment of such an index and how it works is now describedwith reference to FIG. 8.

FIG. 8 shows a plurality of regular expressions (labeled RegEx 70-74). Amapping, such as regular expression to attribute map 76, defines themapping of the regular expressions to attributes. For example, regularexpressions RegEx 70-72 can represent credit card patterns. Theseregular expressions would map to a credit card number attribute. Regularexpressions 73 and 74 may represent phone number patterns and would mapto a phone number attribute. A mapping, in this embodiment, of a regularexpression to an attribute is thus the reservation and usage of thatattribute as implying a successful matching of the regular expression.

In one embodiment, an attribute index 78 is used to represent theattributes in a compact form. In one embodiment, the attribute index 78is implemented as a bit vector. The attribute index 78 can be a vectorof bits with one bit position associated with each defined attribute.For example, in one embodiment, the attribute index 78 has 128 bits. Insuch an embodiment, 128 separate attributes can be defined and occurindependently of one another.

The association of bit positions with attributes can be maintained in atable. Such a table, for this example, would associate bit position Awith the credit card number attribute, and bit position B with the phonenumber attribute. Since, in this example, regular expressions RegEx70-72 would map to the credit card attribute, observing any one of thepatterns defined by RegEx 70-72 would cause bit position A to be set toshow the presence of a credit card number in the captured object.

Setting a bit position can be done by changing a bit either from 0 to 1or from 1 to 0 depending on which one is the default value. In oneembodiment, bit positions are initialized as 0 and are set to 1 to showthe presence of an attribute. Similarly, since regular expressions 73and 74 map to the phone number attribute, observing any one of thepatterns defined by RegEx 73 and 74 would cause bit position B to be set(e.g., to 1) to show the presence of a phone number in the capturedobject.

With the above understanding of how one embodiment of the attributeindex 78 works, one embodiment of the attribute module 64 is nowdescribed with reference to FIG. 9. The input of the attribute module64, as set forth above, is a captured object captured by the objectcapture and assembly modules. The object may be a word document, email,spreadsheet, or some other document that includes text or othercharacters that can represent a pattern expressed as a regularexpression.

In one embodiment, the text content contained in the object is firstextracted to simplify the attribute tagging processing. The text contentof objects includes only textual characters without formatting orapplication context. In one embodiment, the object or the text extractedfrom the object is provided to parser 80. The parser 80 parses theobject to identify which regular expressions appear in the object.

In one embodiment, the parser accesses a regular expression table 82that lists all the regular expressions of interest. The parser 80 thencan determine which of the regular expressions appear in the object orthe text extracted from the object.

In one embodiment, the regular expression table 82 also associates eachregular expression contained therein with an attribute. In this manner,the regular expression table 82 can function as the regular expressionto attribute map 76 illustrated in FIG. 8. For example, the regularexpression table 82 shown in FIG. 9 maps regular expression A toattribute X, regular expressions B and C to attribute Y, and regularexpressions D, E, and F to attribute Z.

Since the regular expression table 82 contains the regular expressionsand their attribute mapping, the parser 80, by parsing the regularexpressions over the object can determine which attributes are presentin the object. In one embodiment, the parsing can be made faster by onlyparsing the regular expressions related to attributes that have not yetbeen found in the object. For example, if the parser finds a hit fromregular expression D in the object, then attribute Z is found in theobject. This makes parsing using regular expressions E and Funnecessary, since attribute Z is already hit.

In one embodiment, the parser 80 outputs a list of attributes found inthe object. As explained above, an attribute can be a category ofpatterns such as credit card number, phone numbers, email addresses,bank routing numbers, social security numbers, confidentiality markers,web sites, the names of executive officers of a company, medicalconditions or diagnoses, confidential project names or numerical stringsindicating salary or compensation information.

In one embodiment, the attributes found in the object are provided to anindex generator 84. In one embodiment, the index generator 84 generatesthe attribute index 78 described with reference to FIG. 8. In oneembodiment, the index generator 84 accesses an attribute table 86. Theattribute table 86 contains the mapping of attributes to bit positionsof the attribute index 78. For example, in FIG. 9, attribute X is mappedto bit position 1, attribute Y is mapped to bit position 2, andattribute Z is mapped to bit position 3.

As an example, if an object contained regular expression A, D, and F,then the parser 80 would first note that attribute X has been hit. Whenrecognizing regular expression D, the parser 80 would note thatattribute Z has been hit. Since these are the only attributes in thisabbreviated example, the parser 80 would provide attributes X and Z tothe index generator 84. According to the attribute table 86, the indexgenerator would set bit positions 1 and 3 of an attribute index 78.Thus, for this simplified example, the attribute index 78 would be“101.”

The generation of an attribute index 78 and the use of the specificmapping tables shown in FIG. 9 is just one example of an attributemodule 64 performing attribute tagging. In another embodiment, eachpossible attribute can have a separate field in the tag associated withthe object indicating whether the attribute is present in the object.Thus, an attribute index can be thought of as a summary of a pluralityof attribute fields. Alternately, each bit position of the attributeindex can be thought of as a separate field. Various otherimplementations and visualizations are also possible.

One embodiment of attribute tagging is now described with reference toFIG. 10. In block 102, and object is captured. In block 104, the textualcontent is extracted from the object. In block 106, a determination ismade as to whether a regular expression appears in the extracted text.

If the regular expression under consideration does not appear in thetext, then, processing continues again at block 106 using the nextregular expression on the regular expression list. If, however, theregular expression under consideration does appear in the text, then, inblock 108 the attribute associated with the regular expression istagged. This can be done by setting a field or position in an index in atag of metadata associated with the object.

In block 110, all other regular expressions associated with the observedattribute are removed from future consideration with respect to theobject. In block 112, a determination is made as to whether attributetagging has completed with respect to the object. If no regularexpressions remain to be compared with the extracted text, then theattribute tagging is complete and processing terminates. Otherwise,processing continues at block 106 with the next regular expression onthe list under consideration.

Several embodiments of how issuing a query over captured objects isimproved by using the attributes described above is now described withreference to FIG. 11. In block 1102, a query is issued. The query can bereceived by the capture device 22 via user interface 34. The processdescribed with reference to FIG. 10 can be implemented entirely withinthe user interface, within some query module of the user interface, or aseparate query module.

The query—in addition to other limitations, such as content type, size,time range, and so on—can contain one or more attributes the user islooking for. For example, the query could be for all Microsoft Exceldocuments from last week containing credit card numbers, credit cardnumbers being an attribute.

In an alternate scenario, the received query only includes one or moreregular expressions, as shown in block 1104. In one such embodiment, inblock 1106, the regular expression is matched to an attribute, ifpossible. For example, if the regular expression in the query is onlysatisfied if another regular expression associated with an attribute issatisfied, then, objects having this attribute tagged are more relevantfor this query than objects in general. In particular, any objectsatisfying the regular expression would also satisfy the attribute. Forexample, a query for a specific credit card number or range will satisfythe credit card attribute.

Whether provided by the user, or identified based on the query, in block1108, the appropriate attribute or attributes are used to eliminateobjects from the query. In one embodiment, a search is done over theappropriate attribute field or index bit positions in the tags in thetag database. If the attributes being sought are not shown as present inan object, the object is eliminated from further consideration for thisquery.

In block 1110, the object remaining after elimination are retrieved fromthe medium they are stored on (such as disk) into memory. They can nowbe presented to the user as query results, or object can be furthereliminated by parsing the retrieved objects for the specific regularexpression queried for, where no specific attribute was named.

In one embodiment, the attributes are completely user-configurable. Theuser interface 34 can provide an attribute editor that allows a user todefine attributes by creating attributes and associating a group ofregular expressions with the created attribute. The capture device 22may come preset with a list of common or popular attributes that may betailored specifically to the industry into which the capture device 22is sold.

In one embodiment, the capture device 22 can create new attributesautomatically. For example, the capture device 22 may observe that acertain regular expression is being searched with some thresholdfrequency (generally set to be above normal). The capture device 22 maycreate an attribute to be associated with this regular expression, andbegin tagging the newly defined attribute when capturing new objects. Inanother embodiment, the capture device may suggest that a new attributebe created when a regular expression is searched frequently. In yetanother embodiment, the capture device 22 may suggest that an attributebe deleted if infrequently used to make room for another more usefulattribute.

General Matters

In several embodiments, the capture system 22 has been described aboveas a stand-alone device. However, the capture system of the presentinvention can be implemented on any appliance capable of capturing andanalyzing data from a network. For example, the capture system 22described above could be implemented on one or more of the servers 14 orclients 16 shown in FIG. 1. The capture system 22 can interface with thenetwork 10 in any number of ways, including wirelessly.

In one embodiment, the capture system 22 is an appliance constructedusing commonly available computing equipment and storage systems capableof supporting the software requirements. In one embodiment, illustratedby FIG. 6, the hardware consists of a capture entity 46, a processingcomplex 48 made up of one or more processors, a memory complex 50 madeup of one or more memory elements such as RAM and ROM, and storagecomplex 52, such as a set of one or more hard drives or other digital oranalog storage means. In another embodiment, the storage complex 52 isexternal to the capture system 22, as explained above. In oneembodiment, the memory complex stored software consisting of anoperating system for the capture system device 22, a capture program,and classification program, a database, a filestore, an analysis engineand a graphical user interface.

Thus, a capture system and a word indexing scheme for the capture systemhave been described. In the forgoing description, various specificvalues were given names, such as “objects,” and various specific modulesand tables, such as the “attribute module” and “general expressiontable” have been described. However, these names are merely to describeand illustrate various aspects of the present invention, and in no waylimit the scope of the present invention. Furthermore various modulescan be implemented as software or hardware modules, or without dividingtheir functionalities into modules at all. The present invention is notlimited to any modular architecture either in software or in hardware,whether described above or not.

The invention claimed is:
 1. A method to be executed by a processor inan electronic environment, comprising: generating an attribute index foran object in a network environment; inserting the attribute index into atag associated with the object, wherein the attribute index indicatesthe presence, in the object, of a portion of a set of attributes;storing the tag with a plurality of other tags, wherein each tagincludes a corresponding attribute index and is associated with acorresponding object; receiving a search query comprising searchattributes, wherein the search attributes are included in anotherportion of the set of attributes; conducting a first search of theattribute index of each of the stored tags for the search attributes;and eliminating a portion of the objects from the first search, whereinthe portion of the objects do not include the search attributes.
 2. Themethod of claim 1, wherein the generating the attribute index comprises:extracting a text content in the object; conducting a second search ofthe text content for an attribute in the set of attributes; setting anindex bit corresponding to the attribute in the attribute index if theattribute is found in the text content, wherein the attribute indexincludes a plurality of index bits corresponding to attributes in theset of attributes; and repeating the second search and the setting theindex bit for all attributes in the set of attributes.
 3. The method ofclaim 2, wherein each attribute in the set of attributes is associatedwith one or more distinct regular expressions, and wherein if one of theregular expressions associated with the attribute is found in the textcontent, eliminating the other regular expressions associated with theattribute from the second search.
 4. The method of claim 3, wherein textcontent is extracted and provided to a parser to identify the regularexpressions in the object.
 5. The method of claim 4, wherein the parseraccesses a regular expression table that includes a list of configuredregular expressions of interest for search querying and wherein theregular expression table associates each of the regular expressions itcontains with at least one attribute in the set of attributes.
 6. Themethod of claim 2, wherein the index bit corresponding to the attributeis determined from an attribute table containing a mapping of attributesto index bits.
 7. The method of claim 1, wherein the object is either aWord document, an e-mail, a spreadsheet, or a PDF having text that canrepresent a pattern expressed as a regular expression.
 8. The method ofclaim 1, wherein the set of attributes include: a credit card number, acredit card number attribute, a phone number attribute, an emailaddresses attribute, a bank routing number attribute, a social securitynumber attribute, a confidentiality marker attribute, a web siteattribute, an executive officers list attribute, a medical conditionattribute, a medical diagnoses attribute, a confidential project nameattribute, and a salary or compensation attribute.
 9. The method ofclaim 1, further comprising: determining a content type of the object;and inserting the content type into a content field associated with thetag.
 10. An apparatus, comprising: a processor; a memory for storingdata and configured to be accessed by the processor, wherein the memoryand the processor co-operate such that the apparatus is configured for:generating an attribute index for an object in a network environment;inserting the attribute index into a tag associated with the object,wherein the attribute index indicates the presence, in the object, of aportion of a set of attributes; storing the tag with a plurality ofother tags, wherein each tag includes a corresponding attribute indexand is associated with a corresponding object; receiving a search querycomprising search attributes, wherein the search attributes are includedin another portion of the set of attributes; conducting a first searchof the attribute index of each of the stored tags for the searchattributes; and eliminating a portion of the objects from the firstsearch, wherein the portion of the objects do not include the searchattributes.
 11. The apparatus of claim 10, wherein the generating theattribute index comprises: extracting a text content in the object;conducting a second search of the text content for an attribute in theset of attributes; setting an index bit corresponding to the attributein the attribute index if the attribute is found in the text content,wherein the attribute index includes a plurality of index bitscorresponding to attributes in the set of attributes; and repeating thesecond search and the setting the index bit for all attributes in theset of attributes.
 12. The apparatus of claim 10, wherein the set ofattributes include: a credit card number, a credit card numberattribute, a phone number attribute, an email addresses attribute, abank routing number attribute, a social security number attribute, aconfidentiality marker attribute, a web site attribute, an executiveofficers list attribute, a medical condition attribute, a medicaldiagnoses attribute, a confidential project name attribute, and a salaryor compensation attribute.
 13. Logic encoded in one or morenon-transitory tangible media that includes code for execution and whenexecuted by a processor operable to perform operations comprising:generating an attribute index for an object in a network environment;inserting the attribute index into a tag associated with the object,wherein the attribute index indicates the presence, in the object, of aportion of a set of attributes; storing the tag with a plurality ofother tags, wherein each tag includes a corresponding attribute indexand is associated with a corresponding object; receiving a search querycomprising search attributes, wherein the search attributes are includedin another portion of the set of attributes; conducting a first searchof the attribute index of each of the stored tags for the searchattributes; and eliminating a portion of the objects from the firstsearch, wherein the portion of the objects do not include the searchattributes.
 14. The logic of claim 13, wherein the generating theattribute index comprises: extracting a text content in the object;conducting a second search of the text content for an attribute in theset of attributes; setting an index bit corresponding to the attributein the attribute index if the attribute is found in the text content,wherein the attribute index includes a plurality of index bitscorresponding to attributes in the set of attributes; and repeating thesecond search and the setting the index bit for all attributes in theset of attributes.
 15. The logic of claim 14, wherein each attribute inthe set of attributes is associated with one or more distinct regularexpressions, and wherein if one of the regular expressions associatedwith the attribute is found in the text content, eliminating the otherregular expressions associated with the attribute from the secondsearch.
 16. The logic of claim 15, wherein text content is extracted andprovided to a parser to identify the regular expressions in the object.17. The logic of claim 16, wherein the parser accesses a regularexpression table that includes a list of configured regular expressionsof interest for search querying and wherein the regular expression tableassociates each of the regular expressions it contains with at least oneattribute in the set of attributes.
 18. The logic of claim 14, whereinthe index bit corresponding to the attribute is determined from anattribute table containing a mapping of attributes to index bits. 19.The logic of claim 13, wherein the object is a Word document, an e-mail,a spreadsheet, or a PDF having text that can represent a patternexpressed as a regular expression.
 20. The logic of claim 13, whereinthe set of attributes include: a credit card number, a credit cardnumber attribute, a phone number attribute, an email addressesattribute, a bank routing number attribute, a social security numberattribute, a confidentiality marker attribute, a web site attribute, anexecutive officers list attribute, a medical condition attribute, amedical diagnoses attribute, a confidential project name attribute, anda salary or compensation attribute.